The learned-based prompt [1]}, [2]} recently show great potential for adapting the large-scale pre-training language/vision-language models on specific downstream tasks. We also explored this technique in our approach. Specifically, an input sentence of text encoder in CLIP model is a sequence of token, and this token sequence contains two token type: \([p]\)  indicates the prompt token and \([cat]\)  indicates the category token. A generalized prompted text can be formulated as \([p_0]...[p_i][cat_0]...[cat_n]...[p_m]\) , where \(n\)  is the number of category token and \(m\)  is the number of prompt token. In learning-based prompt, the prompt tokens \([p_0][p_1]....[p_m]\)  are a set of learnable parameters that can be trained on the data of seen classes and can generalize to unseen classes.
